Details for model architecture and training settings basically follow [1]}, [2]}.
We employ “tf_efficientnetv2_m_in21ft1k” in timm [3]} library as a backbone.
The weights of the backbone were pre-trained on the ImageNet-21K [4]}.
Model training is conducted by using the stochastic gradient
descent with momentum set to 0.9.
The dimension of the descriptor is set to 256, which is specified by the competition rules.
For contrastive loss, we set 0.0 for the positive margin, 1.0 for the negative margin, and 20,000 for the memory size.
For data augmentation, we use AugLy [5]} library.
For nearest neighbor search, we use faiss [6]} library.
